115 lines
2.9 KiB
Python
115 lines
2.9 KiB
Python
"""Main script for GIS classification.
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Configure parameters below to run classification.
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"""
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import os
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from src import (
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GISClassifier,
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RandomForestStrategy,
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SVMStrategy,
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LogisticRegressionStrategy,
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MLEStrategy,
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)
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# ==================== PARAMETERS ====================
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# Input files
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RASTER_PATH = os.path.join("data", "landsat.tif") # Path to GeoTIFF (Landsat)
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VECTOR_PATH = os.path.join("data", "polygons.shx") # Path to Shapefile
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# Column in Shapefile containing class labels
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CLASS_COLUMN = "macroclass"
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# Output file for classified raster
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OUTPUT_PATH = os.path.join("output", "classified.tif")
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# Classification strategy parameters
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# Change strategy by uncommenting desired option:
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# Option 1: Random Forest (recommended for GIS)
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STRATEGY = RandomForestStrategy(
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n_estimators=100,
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max_depth=None,
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random_state=42,
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)
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# # Option 2: Support Vector Machine
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# STRATEGY = SVMStrategy(
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# kernel="linear", # Fast prediction; use 'rbf' for better accuracy but much slower
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# C=1.0,
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# random_state=42,
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# )
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# Option 3: Logistic Regression
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# STRATEGY = LogisticRegressionStrategy(
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# penalty="l2",
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# C=1.0,
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# max_iter=1000,
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# random_state=42,
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# )
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# Option 4: Maximum Likelihood Estimation (classic for GIS)
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# STRATEGY = MLEStrategy(
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# reg_covar=1e-6,
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# )
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# Training parameters
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TEST_SIZE = 0.2 # Fraction for validation
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RANDOM_STATE = 42 # Random seed
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# ==================== RUN CLASSIFICATION ====================
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def main():
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# Check input files exist
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if not os.path.exists(RASTER_PATH):
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print(f"Error: Raster file not found: {RASTER_PATH}")
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return
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if not os.path.exists(VECTOR_PATH):
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print(f"Error: Vector file not found: {VECTOR_PATH}")
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return
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# Create output directory if needed
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os.makedirs(os.path.dirname(OUTPUT_PATH), exist_ok=True)
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# Initialize classifier with strategy
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print(f"Using classification strategy: {STRATEGY.name}")
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print(f"Strategy parameters: {STRATEGY.get_params()}")
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print()
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classifier = GISClassifier(strategy=STRATEGY)
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# Train
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print("Training classifier...")
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metrics = classifier.train(
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raster_path=RASTER_PATH,
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vector_path=VECTOR_PATH,
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class_column=CLASS_COLUMN,
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test_size=TEST_SIZE,
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random_state=RANDOM_STATE,
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)
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print(f"Training samples: {metrics['train_samples']}")
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print(f"Validation samples: {metrics['val_samples']}")
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print(f"Accuracy: {metrics['accuracy']:.2%}")
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print(f"Classes: {metrics['classes']}")
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print()
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# Predict
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print(f"Classifying raster: {RASTER_PATH}")
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result = classifier.predict(
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raster_path=RASTER_PATH,
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output_path=OUTPUT_PATH,
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)
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print(f"Classification complete!")
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print(f"Output saved to: {OUTPUT_PATH}")
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print(f"Output shape: {result.predicted_array.shape}")
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print(f"Classes in output: {result.classes}")
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if __name__ == "__main__":
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main()
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